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About This Role
Join our Team as a Research Engineer!
The Role:
We're looking for an innovative Research Engineer to explore emerging technologies and build proof\-of\-concept prototypes that advance robotics, automation, and operator assist technologies for next\-generation forklift and material handling equipment. You'll partner with cross\-functional teams to turn new ideas into practical technologies that improve operator effectiveness, productivity, and the customer experience.
What You'll Be Doing:
- Explore emerging technologies in robotics, automation, perception systems, computer vision, artificial intelligence (AI), and machine learning.
- Design, build, and evaluate proof\-of\-concept prototypes.
- Partner with mechanical, electrical, software, and robotics engineers to bring new concepts to life.
- Design and execute experiments to validate concepts and system performance.
- Analyze test data and recommend enhancements.
- Share technical findings through reports, presentations, and engineering documentation.
- Support patent development, technical publications, and future product concepts.
- Identify opportunities to improve safety, productivity, and customer value.
What We're Looking For:
- Bachelor's degree in Mechanical Engineering, Electrical Engineering, Computer Engineering, Robotics, Industrial Engineering, Computer Science, or a related engineering discipline.
- Strong analytical, problem\-solving, and communication skills.
- Proficiency in Python, C/C\+\+, Java, or similar programming languages.
- Familiarity with robotics and simulation tools such as ROS, MATLAB, Simulink, or Gazebo.
- Ability to manage multiple priorities while working independently and collaboratively.
- Curiosity, adaptability, and a desire to explore emerging technologies.
What Sets You Apart:
- Background in robotics, automation, computer vision, artificial intelligence (AI), machine learning, or autonomous systems.
- Hands\-on experience designing, building, and testing prototypes.
- Internship, co\-op, or professional experience in research and development (R\&D).
- Familiarity with sensor integration, perception systems, autonomous vehicles, or mobile robotics.
- Experience in the material handling industry.
Where and When You'll Work:
- This is an in\-person role located at our Greene, New York office—ideal for those who thrive in a hands\-on, team\-oriented environment.
What Your Total Compensation \& Benefits Package will look like:
Salary \- $65,440 \- $83,895 per year. Compensation depends on the selected candidate's education and experience.
Relocation: This role may be eligible for relocation assistance.
World Class Benefits:
- Competitive Salary
- Performance Bonus
- Generous Paid Time Off and 13 Paid Holidays
- Affordable Medical plans and no\-cost Dental \& Vision options
- 100% 401(k) match up to 6%
- Company\-Paid Life Insurance, Short\-Term Disability, and Long\-Term Disability
- Tuition Assistance Program
- Employee Assistance Program (EAP) with access to mental health care, legal support, and financial guidance
- Recognition and Kaizen (continuous improvement) Reward Programs
- Meaningful opportunities for personal and professional development
- Best in class work culture!
Big Name \| Big Opportunities \| Life is Better at Toyota
Toyota Material Handling North America (TMHNA), the industry leader in forklift sales, comprises two main brands: Toyota Material Handling and The Raymond Corporation. We believe investing in the best people, products, and processes will fuel our future success, and we will always be driven by our foundational principles of “respect for people” and “continuous improvement”. With opportunities across North America, we are confident you will find the right position within TMHNA that can help you build a long, fulfilling career.
Learn more here: https://www.toyotaforklift.com/careers
Follow us on Social Media: Working at Toyota Material Handling \| Glassdoor
Additional Details : Toyota Material Handling North America will pursue visa sponsorship for this position, as needed, for eligible candidates.
Salary Context
This $65K-$83K range is in the lower quartile for Research Engineer roles in our dataset (median: $207K across 63 roles with salary data).
View full Research Engineer salary data →Role Details
About This Role
Research Engineers bridge the gap between research and production. They implement papers, build experiment infrastructure, optimize training pipelines, and make research prototypes production-ready. They're the engineers who make research work at scale.
The role sits at a unique intersection. You need to understand the math well enough to implement novel architectures correctly, and you need the engineering chops to make them run efficiently on distributed systems. When a research scientist has a breakthrough idea, you're the person who turns it from a notebook prototype into a training pipeline that runs on 256 GPUs.
Across the 4,317 AI roles we're tracking, Research Engineer positions make up 2% of the market. At Toyota Material Handling, this role fits into their broader AI and engineering organization.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
What the Work Looks Like
A typical week involves: implementing a new attention mechanism from a recent paper, profiling and optimizing a training pipeline that's bottlenecked on data loading, building evaluation infrastructure for a new benchmark, debugging distributed training issues across a GPU cluster, and pair-programming with a research scientist on their latest experiment. The work is deeply technical.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
Skills Required
Strong software engineering fundamentals plus ML knowledge. Python, C++, and CUDA experience are common requirements. You'll need to read papers and turn ideas into working code. Distributed systems experience (especially distributed training) is highly valued. Performance optimization skills separate great candidates from good ones.
Experience with large-scale training infrastructure (FSDP, DeepSpeed, Megatron), GPU programming (CUDA, Triton), and the internals of ML frameworks (PyTorch internals, custom autograd functions) is what makes candidates stand out. The best research engineers can debug issues that span the full stack from GPU memory management to numerical precision to algorithmic correctness.
Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
Compensation Benchmarks
Research Engineer roles pay a median of $272,100 based on 227 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($74K) sits 73% below the category median. Disclosed range: $65K to $83K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and AI Engineering Manager ($244,000). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Toyota Material Handling AI Hiring
Toyota Material Handling has 1 open AI role right now. They're hiring across Research Engineer. Based in Greene, NY, US. Compensation range: $83K - $83K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into Research Engineer roles include Software Engineer, ML Engineer, Research Intern.
From here, career progression typically leads toward Senior Research Engineer, Research Scientist, ML Architect.
This is one of the best entry points into AI research without a PhD. Build a strong engineering portfolio with ML projects, contribute to open-source ML frameworks, and demonstrate that you can implement complex ideas correctly and efficiently. The transition to Research Scientist is possible with published first-author work, which some research engineer roles support.
What to Expect in Interviews
Technical screens test both engineering skill and research understanding. Expect coding rounds with performance-critical implementations (GPU optimization, efficient data loading). Be prepared to discuss papers relevant to the team's research area and explain how you'd implement key ideas. System design questions focus on training infrastructure: distributed training, experiment tracking, and compute resource management.
When evaluating opportunities: Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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